Code Benchmarks Should Prioritize Rigor, Reliability, and Reproducibility
📰 ArXiv cs.AI
Learn why rigor, reliability, and reproducibility matter in code benchmarks for large language models and how to prioritize them
Action Steps
- Conduct a thorough survey of existing code benchmarks to identify areas for improvement
- Apply rigorous evaluation metrics to assess benchmark quality
- Implement reproducibility checks to ensure consistent results
- Prioritize reliability in benchmark design to reduce variability
- Develop and share best practices for creating high-quality code benchmarks
Who Needs to Know This
Machine learning engineers, researchers, and developers can benefit from understanding the importance of high-quality benchmarks to evaluate and improve large language models
Key Insight
💡 High-quality benchmarks are crucial for accurately evaluating large language models and driving progress in AI research
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🚀 Prioritize rigor, reliability, and reproducibility in code benchmarks for large language models 🚀
Key Takeaways
Learn why rigor, reliability, and reproducibility matter in code benchmarks for large language models and how to prioritize them
Full Article
Title: Code Benchmarks Should Prioritize Rigor, Reliability, and Reproducibility
Abstract:
arXiv:2501.10711v5 Announce Type: replace-cross Abstract: Code-related benchmarks play a critical role in evaluating large language models (LLMs), yet their quality fundamentally shapes how the community interprets model capabilities. In the past few years, awareness of benchmark quality has grown. Yet, after a decade-scale (2014-2025) survey over 672 code benchmarks, we observed a lag between growing awareness and actual practice. For example, in 2025 alone, the number of benchmarks that ignore
Abstract:
arXiv:2501.10711v5 Announce Type: replace-cross Abstract: Code-related benchmarks play a critical role in evaluating large language models (LLMs), yet their quality fundamentally shapes how the community interprets model capabilities. In the past few years, awareness of benchmark quality has grown. Yet, after a decade-scale (2014-2025) survey over 672 code benchmarks, we observed a lag between growing awareness and actual practice. For example, in 2025 alone, the number of benchmarks that ignore
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